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The above calculations indicate that the presence of technical noise poses no serious problem for the statistical inference of correlation measures from microarray data, let alone two-sample comparisons.
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Let X be a microarray data matrix, let g = (g1,…, g n ) be the vector of group labels, and let g∗ be a random rearrangement of g.
These microarray data have been submitted to Gene Omnibus and are accessible through accession number GSE73888.
Microarray data have been uploaded to the NCBI GEO microarray repository.
The qRT-PCR results largely confirmed the microarray data except for let-7g, whose expression did not show significantly different among three groups.
In summary, we show that combining microarray data with motif analysis, lets us distinguish between the genes that are direct targets of a transcription factor and those that are modulated because of secondary effects.
Then to validate the microarray data, a group of miRNAs (let-7a, mir-328, mir130a, mir-149, mir-602, mir-92b, and mir-198) was selected for RT-PCR analysis.
The microarray data showed that several microRNAs including let-7, miR-923, miR-202, miR-21 and miR-145 were highly expressed in the porcine testes (Table S2).
To confirm the microarray data results, the changes in miRNA expression for let-7a and let-7b were validated by RT-PCR.
Similar methods let Hardin and Wilson (2009) conclude that microarray data require heavy-tailed noise models.
Most of the included studies have associated microarray data.
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